Recipient Organization
UNIVERSITY OF TENNESSEE
2621 MORGAN CIR
KNOXVILLE,TN 37996-4540
Performing Department
Biosystems Engineering and Soil Science
Non Technical Summary
Recent advance in Precision Livestock Farming (PLF) technology has made it possible to do continuous monitoring of various environmental and animal parameters at relatively low costs. The big challenge for the PLF technology is to implement laboratorial development in the field since so far, few PLF technology has proven Return On Investment (ROI) in commercial broiler houses. This challenge must be addressed to develop a precision feeding system as both the feed intake and weight gain needs to be monitored in commercial settings. Therefore, we propose to develop a low-cost precision feeding device to continuously monitoring broiler feed intake and body weight that is suitable for commercial broiler houses. The final goal is to have an improved process control of the grwoth of broilers to produce more animal meat with less feed supply and less environmental impact. The goal of this project is to develop a device for precision feeding of broilers based upon vision and sound analysis.
Animal Health Component
90%
Research Effort Categories
Basic
10%
Applied
90%
Developmental
(N/A)
Goals / Objectives
The worldwide demand for more animal products is increasing while at the same time the concern about animal welfare, animal health, and environmental impact by the livestock sector is rising worldwide. In this project, we are addressing this challenge in the broiler industry by developing a novel precision feeding technology. The ultimate goal is to have an improved process control of the growth of broilers to produce more animal meat with less feed supply and less environmental impact. The objective of this project is to develop a device, named AI-feeder pan, for precision feeding of broilers based upon vision and sound analysis.
Project Methods
We propose to develop a microcontroller powered sensing system and integrate it to a commercial broiler feeder. The sensing system will combine computer vision for broiler weight estimation and sound analysis for feed intake estimation.The developed system will be monitoring every broiler that comes to the AI feeder pan and measure the feed intake at the feeder pan and the weight distribution of birds in each unit time. This data will allows us to analyze the detailed relationship between the input (feed intake) and out (body weight) in the system. The efforts will be focused on two objectives:(1)Development of an integrated sensing system with TinyML for accurate measurement of broiler body weights and feed intake; and (2)Integration of the sensing system with a model-based controller for the optimization of broiler growth trajectories at laboratory scale. Evaluation will done in the third year toValidate the accuracy of the feed intake and body weight measurement in a commercial broiler farm.